Optimizing and controlling the energy consumption of a building

ABSTRACT

Described herein are methods and systems, including computer program products, for determining a load control schedule for energy control devices using a load shifting optimization model and applying the load control schedule to adjust the energy control devices. A server receives thermodynamic models, energy price data and energy load forecast data. The server generates price probability distribution curves based upon the price data and load probability distribution curves based upon the load forecast data. The server executes a load shifting optimization model to determine a profit probability distribution curve for demand response decision rules. The server determines a profit curve that has an optimal profit value. The server generates a load control schedule based upon the optimal profit curve and generates operational parameters for energy control devices using the load control schedule. The server transmits the operational parameters to the energy control devices to adjust operational parameters.

RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/588,699, filed Jan. 2, 2015, which is a continuation-in-partof U.S. patent application Ser. No. 13/729,501, filed Dec. 28, 2012,which claims priority to Ser. No. 61/589,639, filed on Jan. 23, 2012,the entire disclosure of which is incorporated herein by reference.

TECHNICAL FIELD

The technology relates generally to optimizing and controlling theenergy consumption of a building.

BACKGROUND

Weather is the largest variable impacting home energy demand. Many homesare equipped with a standard thermostat to regulate heating and cooling,where the occupant either manually adjusts the temperature to accountfor weather conditions or the thermostat automatically adjuststemperature based on a predetermined schedule. The automatic adjustmentof temperature may be conducted by a utility that provides power to thehome, but often such adjustments are based on incomplete or inaccurateweather information for the precise location of the home and do notfactor in the occupant's personal preferences. In addition, thesesystems are generally not capable of accounting for the thermalcharacteristics of the particular building in which the thermostat isinstalled.

As a result, such systems react to current weather conditions andtemperature needs of the home, rather than performing pre-heating and/orpre-cooling based on forecast weather conditions and the energycharacteristics of the home.

SUMMARY

The techniques described herein relate to optimizing energy use of abuilding (e.g., home) by dynamically controlling comfort devices of thebuilding (such as thermostats, fans, shades, doors, windows,humidifiers, appliances, other heating/cooling systems) to change thecomfort characteristics of the building such as pre-heating,pre-cooling, and the like in response to local weather forecastconditions and when a demand response event is anticipated. In addition,the techniques provide the advantage of maintaining a desired comfortlevel for occupants of the building while encouraging efficient energyusage and monitoring.

In one aspect, the invention features a method for optimizing andcontrolling the energy consumption of a building. A first computingdevice receives one or more measurements from a plurality of sensors, atleast some of which are located inside the building, where themeasurements include temperature readings and comfort characteristics.The first computing device generates a set of thermal responsecoefficients for the building based on energy characteristics of thebuilding, the measurements from the sensors, and weather data associatedwith the location of the building. The first computing device predictsan energy response of the building based on the set of thermal responsecoefficients and forecasted weather associated with the location of thebuilding. The first computing device selects minimal energy requirementsof the building based on an energy consumption cost associated with thebuilding and determines one or more energy control points for thebuilding based on the energy response and the minimal energyrequirements. The first computing device transmits the energy controlpoints to one or more comfort devices in the building.

In another aspect, the invention features a system for optimizing andcontrolling the energy consumption of a building. The system includes afirst computing device configured to receive one or more measurementsfrom a plurality of sensors, at least some of which are located insidethe building, where the measurements include temperature readings andcomfort characteristics. The first computing device is configured togenerate a set of thermal response coefficients for the building basedon energy characteristics of the building, the measurements from thesensors, and weather data associated with the location of the building.The first computing device is configured to predict an energy responseof the building based on the set of thermal response coefficients andforecasted weather associated with the location of the building. Thefirst computing device is configured to select minimal energyrequirements of the building based on an energy consumption costassociated with the building and determine one or more energy controlpoints for the building based on the energy response and the minimalenergy requirements. The first computing device is configured totransmit the energy control points to one or more comfort devices in thebuilding.

In another aspect, the invention features a computer program product,tangibly embodied in a non-transitory computer readable storage medium,for optimizing and controlling the energy consumption of a building. Thecomputer program product includes instructions operable to cause a firstcomputing device to receive one or more measurements from a plurality ofsensors, at least some of which are located inside the building, wherethe measurements include temperature readings and comfortcharacteristics. The computer program product includes instructionsoperable to cause the first computing device to generate a set ofthermal response coefficients for the building based on energycharacteristics of the building, the measurements from the sensors, andweather data associated with the location of the building. The computerprogram product includes instructions operable to cause the firstcomputing device to predict an energy response of the building based onthe set of thermal response coefficients and forecasted weatherassociated with the location of the building. The computer programproduct includes instructions operable to cause the first computingdevice to select minimal energy requirements of the building based on anenergy consumption cost associated with the building and determine oneor more energy control points for the building based on the energyresponse and the minimal energy requirements. The computer programproduct includes instructions operable to cause the first computingdevice to transmit the energy control points to one or more comfortdevices in the building.

In another aspect, the invention features a system for optimizing andcontrolling the energy consumption of a building. The system includesmeans for receiving one or more measurements from a plurality ofsensors, at least some of which are located inside the building, wherethe measurements include temperature readings and comfortcharacteristics. The system includes means for generating a set ofthermal response coefficients for the building based on energycharacteristics of the building, the measurements from the sensors, andweather data associated with the location of the building. The systemincludes means for predicting an energy response of the building basedon the set of thermal response coefficients and forecasted weatherassociated with the location of the building. The system includes meansfor selecting minimal energy requirements of the building based on anenergy consumption cost associated with the building and determines oneor more energy control points for the building based on the energyresponse and the minimal energy requirements. The system includes meansfor transmitting the energy control points to one or more comfortdevices in the building.

Any of the above aspects can include one or more of the followingfeatures. In some embodiments, the first computing device compares thetemperature readings from one or more sensors to a temperaturemeasurement provided by a thermostat inside the building and adjusts theenergy control points based upon the comparison. In some embodiments,the energy control points include thermostat set points, controlsettings for the comfort devices, and control settings for windowshades. In some embodiments, the comfort characteristics includeoccupancy status of a building area, humidity, radiative heat fromwalls, operational status for the comfort devices, a location of abuilding occupant, a distance of the building occupant from thebuilding, and a travel time for the occupant to arrive at the building.In some embodiments, the first computing device adjusts the energycontrol points based upon the travel time and/or the distance.

In some embodiments, the energy characteristics include one or moretemperature readings from the sensors, a temperature reading from athermostat of the building, a status of an HVAC system in the building,a status of one or more energy sources supplying the building, andstatus of doors and/or windows of the building. In some embodiments, theHVAC system includes one or more stage heating or cooling units. In someembodiments, the energy sources supplying the building include electric,gas, solar, wind, heat pump, and energy control devices.

In some embodiments, generating the set of thermal response coefficientsis further based on physical data of the building. In some embodiments,the physical data comprises at least one of: thermal mass, windinfiltration, relative area of windows, amount of insulation, materialof construction, wind infiltration of the building, and efficiency of anassociated HVAC system. In some embodiments, predicting an energyresponse is further based on the energy consumption cost associated withthe building. In some embodiments, the energy consumption costrepresents an amount of power required to change a comfort level of thebuilding for various external temperatures.

In some embodiments, the minimal energy requirements comprise a powerconsumption amount of an HVAC system in the building and a duty cycle ofthe HVAC system. In some embodiments, determining energy control pointsis further based on weather forecast data, a comfort preference providedby an occupant of the building, or both.

In some embodiments, the energy control points transmitted to thethermostat comprise a schedule for control of the thermostat over aperiod of time. In some embodiments, the first computing device receivesthe weather data from a network of remote sensors. In some embodiments,the first computing device receives thermostat data from a deviceconnected to an HVAC system inside the building.

In some embodiments, the first computing device adjusts the generatedset of thermal response coefficients using error correction. In someembodiments, the error correction includes filtering anomalies from thegenerated set of thermal response coefficients.

In some embodiments, the weather data includes current weatherconditions at the location of the building, forecast weather conditionsfor the location of the building, solar load at the location of thebuilding, or any combination thereof. In some embodiments, the firstcomputing device compares the predicted energy response of the buildingto a predicted energy response of one or more other buildings and ranksthe predicted energy response of the building based on the comparison ofthe predicted energy response. In some embodiments, generating a set ofthermal response coefficients for the building is further based on smartmeter data.

In some embodiments, the plurality of sensors include combination doorstatus and temperature sensors, combination window status andtemperature sensors, combination appliance status and temperaturesensors, combination motion detection and temperature sensors, infraredthermal sensors, standalone temperature sensors, and humidity sensors.In some embodiments, the first computing device receives a signal from acombination door status and temperature sensor, determines whether adoor associated with the combination door status and temperature sensoris open or closed based upon the signal, and identifies an energy lossissue for the door if the door is closed and a temperature reading fromthe combination door status and temperature sensor diverges from atemperature measurement of a thermostat in the building. In someembodiments, the first computing device transmits an alert to a remotecomputing device associated with an occupant of the building if anenergy loss issue is identified. In some embodiments, the alert includesan energy efficiency scorecard for the building and identifies theenergy loss issue.

In some embodiments, the first computing device receives a motiondetection signal and a temperature signal from one or more sensors,determines an occupancy status of an area monitored by the one or moresensors based upon the motion detection signal, and adjusts the energycontrol points based upon the occupancy status and the temperaturesignal. In some embodiments, the comparison step includes determiningwhether changes over time in the sensor temperature measurementscorrespond to changes over time in a temperature measurement of athermostat in the building. In some embodiments, the first computingdevice determines an energy loss issue corresponding to an area of thebuilding in which the sensor is located when the changes over time inthe sensor temperature measurements diverge from the changes over timein the thermostat temperature measurement. In some embodiments, theadjusting step comprises changing the energy control points to accountfor a difference between the sensor temperature measurements and thethermostat temperature measurement.

The invention, in another aspect, features a method for determining aload control schedule for a plurality of energy control devices using aload shifting optimization model and applying the load control scheduleto adjust the plurality of energy control devices. A server computingdevice receives one or more thermodynamic grey-box models, where eachthermodynamic grey-box model is associated with one or more buildings.The server computing device receives energy price data associated with autility provider servicing the one or more buildings and energy loadforecast data associated with the one or more buildings. The servercomputing device generates one or more price probability distributioncurves based upon the energy price data. The server computing devicegenerates one or more load probability distribution curves based uponthe energy load forecast data. The server computing device executes aload shifting optimization model using the one or more price probabilitydistribution curves, the one or more load probability distributioncurves, and an energy sale price to determine a profit probabilitydistribution curve for each of a plurality of demand response decisionrules. The server computing device determines at least one of the profitprobability distribution curves that has an optimal profit value. Theserver computing device generates a load control schedule for each of aplurality of energy control devices coupled to the server computingdevice based upon the profit probability distribution curve that has theoptimal profit value. The server computing device generates one or moreoperational parameters for each of the plurality of energy controldevices using the load control schedule. The server computing devicetransmits the one or more operational parameters to the plurality ofenergy control devices, where the energy control devices adjust one ormore of current and future operational parameters based upon thereceived operational parameters.

The invention, in another aspect, features a system for determining aload control schedule for a plurality of energy control devices using aload shifting optimization model and applying the load control scheduleto adjust the plurality of energy control devices. The system comprisesa server computing device that receives one or more thermodynamicgrey-box models, where each thermodynamic grey-box model is associatedwith one or more buildings. The server computing device receives energyprice data associated with a utility provider servicing the one or morebuildings and energy load forecast data associated with the one or morebuildings. The server computing device generates one or more priceprobability distribution curves based upon the energy price data. Theserver computing device generates one or more load probabilitydistribution curves based upon the energy load forecast data. The servercomputing device executes a load shifting optimization model using theone or more price probability distribution curves, the one or more loadprobability distribution curves, and an energy sale price to determine aprofit probability distribution curve for each of a plurality of demandresponse decision rules. The server computing device determines at leastone of the profit probability distribution curves that has an optimalprofit value. The server computing device generates a load controlschedule for each of a plurality of energy control devices coupled tothe server computing device based upon the profit probabilitydistribution curve that has the optimal profit value. The servercomputing device generates one or more operational parameters for eachof the plurality of energy control devices using the load controlschedule. The server computing device transmits the one or moreoperational parameters to the plurality of energy control devices, wherethe energy control devices adjust one or more of current and futureoperational parameters based upon the received operational parameters.

Any of the above aspects can include one or more of the followingfeatures. In some embodiments, each thermodynamic grey-box model isbased upon one or more characteristics of an indoor environment of abuilding and a flow of energy through an envelope of the building. Insome embodiments, the energy price data is one or more of historicalenergy price data and per-hour price probability data. In someembodiments, the energy load forecast data is one or more of historicalload data and load forecast probability data.

In some embodiments, executing the load shifting optimization modelcomprises: determining a demand response decision rule; generating avector of simulated market prices using at least one of the priceprobability distribution curves; generating a vector of simulated loadsusing at least one of the load probability distribution curves; applyingthe demand response decision rule to the vector of simulated marketprices and the vector of simulated loads to generate the profitprobability distribution curve for the demand response decision rule;and determining a profit value associated with the profit probabilitydistribution curve using the energy sale price. In some embodiments, theserver computing device repeats the execution of the load shiftingoptimization model for each combination of the plurality of demandresponse decision rules, the one or more price probability distributioncurves, and the one or more load probability distribution curves togenerate a plurality of profit values.

In some embodiments, determining at least one of the profit probabilitydistribution curves that has an optimal profit value comprises selectingthe profit probability curve that is associated with a maximum profitvalue. In some embodiments, the server computing device determines arisk value associated with the profit probability distribution curveusing the energy sale price. In some embodiments, determining at leastone of the profit probability distribution curves that has an optimalprofit value comprises selecting the profit probability curve that isassociated with a minimum risk value.

In some embodiments, the one or more operational parameters for each ofa plurality of energy control devices comprise thermostat setpoints. Insome embodiments, the thermostat setpoints comprise a schedule ofcurrent and future temperature settings for the thermostat. In someembodiments, the one or more operational parameters for each of aplurality of energy control devices comprise operational settings for acomfort device. In some embodiments, the operational settings for acomfort device comprise a schedule of current and future operationalsettings for the comfort device.

Other aspects and advantages of the invention will become apparent fromthe following detailed description, taken in conjunction with theaccompanying drawings, illustrating the principles of the invention byway of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages of the invention described above, together with furtheradvantages, may be better understood by referring to the followingdescription taken in conjunction with the accompanying drawings. Thedrawings are not necessarily to scale, emphasis instead generally beingplaced upon illustrating the principles of the invention.

FIG. 1 is a block diagram of a system for optimizing and controlling theenergy consumption of a building.

FIG. 2 is a detailed block diagram of a server computing device foroptimizing and controlling the energy consumption of a building.

FIG. 3 is a flow diagram of a method for optimizing and controlling theenergy consumption of a building.

FIG. 4 is a diagram showing power usage and temperature readings asdetermined by predictions of the system in comparison to actual powerusage and temperature readings.

FIGS. 5A-5B are diagrams showing temperature readings of a temperaturesensor located in proximity to a door as compared to temperaturereadings of the building's thermostat and of the outdoor environmentover the same time period.

FIG. 6 is a diagram showing temperature readings of temperature sensorslocated in four different rooms of a building as compared to temperaturereadings of the building's thermostat and of the outdoor environmentover the same time period.

FIG. 7 is an exemplary scorecard showing energy efficiency and energyusage for a building.

FIG. 8 is a block diagram of a system for determining a load controlschedule for a plurality of energy control devices using a load shiftingoptimization model and applying the load control schedule to adjust theplurality of energy control devices.

FIG. 9 is a flow diagram of a method for determining a load controlschedule for a plurality of energy control devices using a load shiftingoptimization model and applying the load control schedule to adjust theplurality of energy control devices.

FIG. 10 is an exemplary building heat transfer model using thethermal-electric analogy used in the thermodynamic grey-box model.

FIG. 11 is an exemplary graph of a base case profit distributiongenerated by the system.

FIG. 12 is an exemplary graph of a best case profit distributiongenerated by the system.

FIG. 13 is an exemplary graph of a worst case profit distributiongenerated by the system.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a system 100 for optimizing and controllingthe energy consumption of a building. The system 100 includes a servercomputing device 102, a communications network 104, a plurality ofcomfort devices 106 (e.g., a thermostat device 106 a that controls theheating and/or cooling apparatus for the building, other comfort devicessuch as a fan 106 b and window shades 106 c), a plurality of sensordevices 107 a-107 z (collectively, 107), and a client computing device108. The server computing device 102 receives data from external sources(e.g., weather data, thermostat data from thermostat 106 a, sensor datafrom sensors 107) and determines energy response characteristics andenergy requirements for a particular building. The server computingdevice 102 determines energy control points for the building, andtransmits the energy control points to comfort devices 106 in thebuilding (e.g., thermostat 106 a, fan 106 b, window shades 106 c) viathe network 104 so that the comfort devices 106 can adjust theirsettings in order to impact the comfort conditions (e.g.,heating/cooling, humidity, airflow, etc.) of the building appropriately.Energy control points can be settings that affect the operation of thecomfort devices, such as temperature set points and scheduling for athermostat 106 a, settings for fans 106 b and window shades 106 c, andthe like. The server computing device 102 also interfaces with a clientcomputing device 108 via the network 104 to provide a portal (e.g., aweb browser interface) through which a user can view the energy responsecharacteristics and energy requirements for a building (e.g., the user'shouse). The user can also, for example, manually adjust the energycontrol points for the thermostat 106 a and other comfort devices suchas fans 106 b and window shades 106 c, view temperature profiles andrelated environmental conditions for the sensors 107, and set up acomfort profile with the user's preferences so the server computingdevice 102 can automatically adjust the comfort devices 106 based on thecomfort profile. It should be appreciated that although FIG. 1 depictscertain examples of comfort devices 106 a-106 c, other types of comfortdevices can be included in the system 100 without departing from thescope of invention.

FIG. 2 is a detailed block diagram of the server computing device 102for optimizing and controlling the energy consumption of a building. Theserver computing device 102 includes a data receiving module 202, a datastorage 204, a coefficient modeler 206, a predictive outcome module 208,an optimization and scheduling module 210, a data verification module212, a sending module 214, and a web interface module 216. It should beappreciated that, although FIG. 2 shows the components (e.g., 202, 204,206, 208, 210, 212, 214 and 216) as within a single server computingdevice 102, in some embodiments the components are distributed ondifferent physical devices without departing from the spirit or scope ofthe invention. Also, in embodiments where the components are distributedon different physical devices, those devices can reside at the samephysical location or may be dispersed to different physical locations.

The data receiving module 202 provides an interface between externaldata sources (e.g., weather databases, energy providers, comfort devices106, and sensors 107) and the data storage 204 of the server computingdevice 102. The data receiving module 202 receives data associated withatmospheric conditions and weather from various external data collectionand/or monitoring systems (e.g., NWS, NOAA, Earth Networks WeatherNetwork). Other sources of information include, but are not limited to,governmental agencies and third-party private companies. The atmosphericconditions and weather data can include, but is not limited to, currentconditions information, forecast information and weather alertinformation. The atmospheric conditions and weather data can becategorized by location (e.g., zip code or GPS coordinates). The datareceiving module 202 communicates with the various external data systemsand sources via standard communications networks and methods.

The data receiving module 202 also receives information from comfortdevices 106 that are located within buildings and whose operationimpacts the comfort characteristics of the building. As can beappreciated, a primary goal of a building's HVAC system is to controlthe thermal comfort of an indoor environment. Generally, thermal comfortcan be defined as the condition of mind that expresses satisfaction withthe thermal environment. Many factors can influence thermal comfort,such as metabolic rate, clothing insulation, air temperature, meanradiant temperature, air speed, relative humidity, and a subject'spersonal preferences. Therefore, the ability to receive information fromand to control comfort devices such as thermostats operating HVACsystems, fans, doors, windows, heaters, vents, shades and the like isimportant in optimizing and controlling the energy consumption of abuilding.

For example, the thermostat 106 a transmits characteristics about itscurrent operation status (e.g., current temperature setting, heatingmode, cooling mode, power settings, efficiency conditions) to the servercomputing device 102. In another example, comfort devices such as fans106 b and shades 106 c transmit operational settings (e.g., on/off,open/closed, speed) to the server computing device 102. In someembodiments, the data receiving module 202 also gathers information froma smart meter (e.g., electric meter, gas meter, or water meter) locatedat the building. The smart meter is configured to record consumption ofenergy in predetermined intervals (e.g., one hour), and communicate therecorded information to the utility that provides service to thebuilding. In some embodiments, the data receiving module 202 alsogathers information from devices at the building that controlalternative sources of energy supplied to the building, such as solarpanels, wind power, generators and so forth. The data receiving module202 can receive the recorded consumption information and correlate theenergy usage with other types of data (e.g., thermostat data, exteriorweather data) to determine how changes in outside weather conditions andadjustment of the comfort devices' 106 settings impact energyconsumption. It should be appreciated from the foregoing that a buildingmay have multiple thermostats and/or multiple heating and cooling zones,and that the system 200 described herein can conduct the energyoptimization and control process described herein for a plurality ofcomfort devices within the same building.

The data receiving module 202 also receives information from additionaldevices (e.g., sensors 107) that can be positioned at various locationswithin a building. For example, each room in a building may be equippedwith a sensor to provide a measurement of the temperature in thespecific room—which may or may not contain a thermostat. The temperaturereadings provided by the sensor can be compared against the reading(s)obtained from the thermostat to determine whether the temperature in aspecific location (e.g., a room) within a building is diverging from thethermostat and potentially not responding to actions taken by thethermostat to change the temperature of the building. For example, ifthe thermostat initiates an action to heat the building and records acorresponding increase in temperature in the location of the thermostatbut the sensor in another room does not record an increase intemperature, the other room may not be heating properly due tostructural defects (e.g., leaky doors/windows) or problems with theheating/cooling system in the room.

In addition, the sensors can comprise a combination sensor, that is atemperature sensor combined with other types of sensors or devices thatmay be found in a building, such as a door status sensor, a windowstatus sensor, a shades status sensor, an appliance status sensor, or amotion detection sensor. The data receiving module 202 can receivemultiple types of information from the combined sensors that are usefulin determining and optimizing the temperature, comfort, and energy usageof the building. For example, a combined temperature and motion sensorcan provide information relating to the temperature and movement oractivity in a particular room within a building. If the temperature andmotion sensor does not detect any activity in the room between the hoursof 8:00 am and 5:30 pm (e.g., no one is using the room) but thetemperature in the room shows a change based upon a heating or coolingaction initiated by the building thermostat during those times, it maybe a waste of energy to heat or cool the room because the room is notbeing occupied. Therefore, the system can determine adjustments to theheating or cooling profile at the thermostat in order to conserve energyduring that time period.

In another example, a combined door open/close and temperature sensorcan provide information relating to the temperature at a door as well asthe state of the door (e.g., open, closed). In this example, if thesensor indicates that the door is closed but the temperature reading atthe door is significantly different than the temperature in another partof the room or building, it may suggest that the door is leaky, damaged,or not properly insulating. Therefore, the system can generate atemperature profile for the door, include this variable in the overallenergy optimization process, and provide a report or alert highlightingthe temperature discrepancy. Alternatively, if the sensor indicates thatthe door is open and the temperature reading at the door issignificantly different, the system can account for the state of thedoor when conducting the energy optimization process described herein.

In another example, a combined appliance and temperature sensor canprovide information relating to the temperature in proximity to anappliance as well as operating characteristics or conditions of theappliance. In this example, the sensor can indicate that a temperaturearound a stove is higher than in other areas of the room and/or buildingand also indicates that the stove is on during those times. Therefore,the system can account for the temperature variation and makeadjustments to the energy optimization profile and thermostat controlprocess as needed. It should be appreciated that the sensors 107 canmeasure other types of information in addition to or instead oftemperature, such as humidity, radiant heat, sunlight, air flow/speed,and the like.

The data receiving module 202 consolidates and aggregates the receivedinformation into a format conducive for storage in the data storage 204and processing by the modules 206, 208, 210, 212, 214 and 216. Forexample, each data source to which the data receiving module 202 isconnected may transmit data using a different syntax and/or datastructure. The data receiving module 202 parses the incoming dataaccording to an understanding of the source of the data and reformat thedata so that it conforms to a syntax or structure acceptable to the datastorage 204 and the modules 206, 208, 210, 212, 214 and 216. In someembodiments, the external data sources transmit the information in astandard format (e.g., XML) to reduce the processing required of thedata receiving module 202.

The data receiving module 202 communicates with the data storage 204 tosave and retrieve data received from external sources in preparation fortransmitting the data to the modules 206, 208, 210, 212, 214 and 216. Insome embodiments, the data receiving module 202 transmits a notificationto the coefficient modeler 206 that the data has been stored in the datastorage 204 and is ready for processing by the coefficient modeler 206.The notification includes a reference indicator (e.g., a databaseaddress) of the storage location of the data within the data storage204.

The data storage 204 is a database or other similar data structure,including hardware (e.g., disk drives), software (e.g., databasemanagement programming) or both, that stores information received by thedata receiving module 202. The data storage 204 also provides data tothe modules 206, 208, 210, 212, 214 and 216, and receives updated dataand analysis from the modules 206, 208, 210, 212, 214 and 216.

The coefficient modeler 206 is a module that retrieves information fromthe data storage 208 and generates sets of thermal response coefficientsassociated with energy characteristics of a building. The modeler 206determines the location of the building (e.g., by retrieving thebuilding's zip code or GPS coordinates). In some embodiments, themodeler 206 retrieves additional data associated with the building, suchas physical structure of the building (e.g., construction materials),solar orientation and load, thermal mass, and wind infiltration. In someembodiments, the modeler 206 infers the physical structure of thebuilding, solar orientation and load, thermal mass, and/or windinfiltration based on the location of the building. In some embodiments,the modeler 206 retrieves smart meter data associated with the buildingthat has been collected by the server computing device 102 from a smartmeter installed at the building. In some embodiments, the modeler 206extracts data from the data storage 204 in the form of a comma-separatedvalue (.csv) file.

Based on this information, the modeler 206 determines a thermal profilefor the building. Using the thermal profile in conjunction with theweather information for the location of the building, the currentthermostat setting for the building, and other data associated with thebuilding (e.g., smart meter data, sensor data from sensors 107), themodeler 206 generates sets of thermal response coefficients based on thevarious characteristics that affect the comfort of the building (e.g.,temperature, humidity, thermal mass, solar loading, and windinfiltration) and the amount of energy consumed by the heating/coolingapparatus and other comfort devices at the building. Each set of thermalresponse coefficients can be different, according to projections of theweather conditions at the location over a period of time (e.g., an hour,a day). The modeler 206 ranks the sets of thermal response coefficientsbased on considerations of energy usage, forecast accuracy, occupantpreferences, and the like. The modeler 206 transmits the ranked thermalresponse coefficients to the data storage 204 for use by other modules208, 210, 212, 214, 216 of the system 100.

The optimizing and scheduling module 210 retrieves the ranked thermalresponse coefficients from the data storage 204 along with additionalinformation, such as the weather forecast associated with the locationof the building and an occupant preference profile associated with thebuilding. In some embodiments, the optimizing and scheduling module 210also retrieves current and estimated energy prices (e.g., from the datastorage 204 or from an external data source such as a utility company).The optimizing and scheduling module 210 transmits the information tothe predictive outcome module 208.

The predictive outcome module 208 generates a series of energy controlpoints for the comfort devices 106 in the building, based on the currentand forecast weather conditions for that location and each set ofthermal response coefficients. The predictive outcome module 208 alsogenerates a power usage estimate, duty cycle, and indoor temperatureforecast for the heating/cooling apparatus installed the building basedon the series of energy control points. In some embodiments, thepredictive outcome module 208 can also generate an estimated energy costassociated with the series of energy control points by incorporatingcurrent energy prices into the determination.

The optimizing and scheduling module 210 receives the series of energycontrol points from the predictive outcome module 208 and optimizes theresults based on additional factors such as anticipated demand responseevents and/or occupant preferences. For example, if the weather forecastindicates that the exterior temperature will rise from 70° F. at 8:30 amto 90° F. at 11:00 am, the optimizing and scheduling module 210determines that there will be an increased demand for energy to powerair conditioning systems at that time. The optimizing and schedulingmodule 210 also determines that the price of energy will go up at thattime. As a result, the optimizing and scheduling module 210 adjusts theseries of energy control points to provide additional cooling (i.e.,pre-cool) to the home in the earlier part of the morning (e.g., 8:30 am)so that the air conditioner in the home does not need to run as long at11:00 am when the exterior temperature is hotter. For example, theoptimizing and scheduling module 210 can transmit energy control pointsto fan 106 b that instruct the fan to switch to a higher speed duringthe pre-cool phase, then switch to a lower speed or turn off duringother parts of the day.

Also, the optimizing and scheduling module 210 understands that theprice of energy at 8:30 am is lower than the predicted cost at 11:00 am,so an increased consumption of energy in the early morning achieves acost savings versus consuming more energy at the later time of 11:00 am.In some cases, the optimizing and scheduling module 210 can adjust theenergy control points based upon temperature readings and comfortcharacteristics received from the sensors 107 inside the building, asdescribed previously.

Once the optimizing and scheduling module 210 has adjusted the series ofenergy control points, the module 210 transmits the series of energycontrol points to the data storage 204. The data storage 204 transmitsthe series of energy control points to the sending module 214, whichcommunicates the energy control points to the comfort devices 106 in thebuilding. In one example, the energy control points include temperatureset points that provide a schedule of target temperatures for thethermostat 106 a for a given time period (e.g., one day). The thermostat106 a can perform heating and/or cooling according to the schedule oftemperature set points to achieve increased energy efficiency andanticipation of demand response events.

The server computing device 102 also includes a data verification module212. The data verification module 212 retrieves energy usage data forthe building from a prior time period and compares the usage data towhat was predicted by the system 100 for the same time period. Forexample, the data verification module 212 retrieves the energy usagedata (e.g., as provided by a smart meter, a solar panel module, or froma utility) for a customer's home on a particular day. The dataverification module 212 also retrieves the predicted energy usage forthe same day, based on the determinations performed by the modeler 206,predictive outcome module 208 and optimization and scheduling module210. The data verification module 212 compares the two energy usagevalues (actual vs. predicted) to determine if any deviations occurred.Based on the comparison, the data verification module 212 can provideenergy usage savings data that can be presented to the customer (e.g.,via the web interface module 216). In some embodiments, the dataverification module 212 determines energy savings using additionalmethodologies. For example, the data verification module 212 can comparea building's energy usage between (i) a day where the optimization andscheduling module 210 did not adjust the temperature set point schedulefor the building's thermostat and (ii) a day where the optimization andscheduling module 210 did adjust the temperature set point schedule. Thedata verification module 212 can produce charts and other reportsshowing the energy savings achieved when the optimization and schedulingmodule 210 is run. In addition, the comparison information generated bythe data verification module 212 is used to refine the coefficientmodels created by the modeler 206 to achieve greater accuracy and betterefficiency.

The server computing device 102 also includes a web interface module216. The web interface module 216 is configured to receive connectionrequests from client devices (e.g., client device 108 in FIG. 1) andprovide a portal for the client devices to access and update the thermalprofile information associated with a building. For example, a homeownercan register with the system 100 and connect to the web interface module216 via a web browser on a client device 108. Upon logging in, thehomeowner is presented with a portal containing various informationrelated to the current energy characteristics of his home, as well asinteractive features that allow the homeowner to establish and changecomfort preferences for the internal temperature of his home. In someembodiments, the portal includes a home energy audit function whichleverages the data stored in the system 100 (e.g., thermal profile,energy usage, weather conditions, data from sensors 107 throughout thehome) and compares the homeowner's dwelling with other buildings thatshare similar thermal comfort and/or energy consumption characteristics.The homeowner can determine the relative energy usage of his homeagainst other homes or buildings in his area. Based on the home energyaudit, the portal can also provide a customized and prioritized list ofsuggestions for improving the energy efficiency of the building.

FIG. 3 is a flow diagram of a method 300 for optimizing and controllingthe energy consumption of a building. The server computing device 102,using the data receiving module, receives (302) one or more measurementsfrom a plurality of sensors, at least some of which are located insidethe building, where the measurements include temperature readings andcomfort characteristics as described above. The server computing device102, using the coefficient modeler 206, generates (304) a set of thermalresponse coefficients for a building based on energy characteristics ofthe building, measurements obtained from the sensors 107, and weatherdata associated with the location of the building. The server computingdevice 102, using the optimization and scheduling module 210 and thepredictive outcome module 208, predicts (306) an energy response of thebuilding based on the set of thermal response coefficients andforecasted weather conditions associated with the location of thebuilding.

The server computing device 102, using the optimization and schedulingmodule 210 and the predictive outcome module 208, selects (308) minimalenergy requirements of the building based on an energy consumption costassociated with the building. The server computing device 102, using theoptimization and scheduling module 210 and the predictive outcome module208, determines (310) one or more energy control points for the buildingbased on the energy response and the minimal energy requirements.

The server computing device 102, using the data verification module 212,compares the previous day's energy usage for the building against thepredicted energy usage provided by the modeler 206 and the predictiveoutcome module 208 to determine energy usage deviations and potentialenergy savings. The server computing device 102, using the sendingmodule 214, transmits (312) the adjusted energy control points tocomfort devices 106 of the building.

In some embodiments, the techniques described herein are used to executedemand response events in conjunction with local or regional utilitiesand service providers. The predictive modeling and comfort devicecontrol features of the system 100 can be leveraged to prepare forpotential demand response events identified by the utilities, and shiftenergy consumption by buildings connected to the system from peak demandtimes to lower demand times—thereby reducing the energy demand load onthe utilities and potentially providing energy to the buildings at alower cost.

For example, based on the predictive modeling, energy control pointgeneration, and associated analysis, the server computing device 102determines that a certain amount of energy will be consumed by buildingsconnected to the system 100 over the course of the following day. Theserver computing device 102 also determines that, based on weatherforecast information, there may be a peak demand event for energy duringa two-hour window the following day (e.g., due to forecast low/highexternal temperatures or a forecast change in external temperature).Because the server computing device 102 has identified an amount ofenergy that will be potentially used during that two-hour window, theserver computing device 102 can proactively adjust the energy controlpoints for some or all of the comfort devices 106 (e.g., thermostat 106a, fan 106 b, shades 106 c) to reduce or eliminate consumption of energyand to optimize the comfort characteristics of the building during thepeak demand time.

Often, a utility does not have advance warning of a potential demandresponse event. For example, the utility may not anticipate a demandresponse event until one hour before the event begins. At the point whenthe utility becomes aware of the demand response event, the utility caninform the server computing device 102 of the upcoming event. Based onits previous analysis, the server computing device 102 can commit aparticular amount of energy to the utility that will not be consumed bybuildings of the system 100 during the demand response event. If theutility notifies the system 100 that the utility requires the committedamount of energy, the server computing device 102 automaticallytransmits adjusted energy control point schedules to the connectedcomfort devices 106 that reduce energy consumption by the amount ofenergy committed to the utility.

The server computing device 102 can also adjust the energy control pointschedules of the comfort devices 106 to account for the reduced energyconsumption while approximately maintaining the comfort characteristics(e.g., temperature, humidity, and the like) desired by the occupantand/or specified in the schedule. For example, if the server computingdevice 102 understands that the comfort devices 106 will be adjusted toconsume no energy during a demand response event (e.g., mid-afternoon ona summer day), the server computing device 102 can adjust the energycontrol point schedule for the comfort devices 106 (e.g., thetemperature set point schedule for thermostat 106 a) to pre-cool thebuilding in advance of the demand response event so that the temperatureof the building is at or near the originally-scheduled value during theevent. The additional energy consumed by the pre-cooling does not occurduring the demand response event—leading to reduced load on the utilityand potential cost savings for the occupant. Plus, the buildingapproximately maintains desired/scheduled comfort characteristics duringthe event.

Several mathematical algorithms can be used in developing possiblepredictions of the energy consumed by buildings connected to the system100, as well as predicting the specific amount of energy devoted to theoperation of HVAC.

Building Energy Model Predictions

In one embodiment, a building is represented as a grey-box systembalancing the sensible energy of the entire indoor environment with theflow of energy through the envelope. This type of modeling accounts forheat diffusion through the walls, convection on the inner and outerwalls, solar irradiance, infiltration, thermal mass, and HVAC systemperformance. HVAC status data is obtained from internet connectedthermostats, and electricity data from smart meters.

Transient temperatures within the wall are accounted for by solving forthe temperatures at nodes within a uniform property wall using anexplicit tridiagonal matrix algorithm. Inputs to the model includeoutdoor temperature, solar insolation, and wind speed data from localweather stations, indoor air temperature, and HVAC status data frominternet connected thermostats, and electricity data from smart meters.Instead of requiring detailed measurements of building characteristicssuch as insulation R-values and fenestration ratios, effective parametervalues are calculated from the data.

The exemplary solution technique consists of using a Genetic Algorithmto obtain a least squares curve that fits the modeled indoor airtemperatures to the measured temperatures. The parameters are updatedperiodically to account for changes in the weather and building status.Energy forecasts are made by running the model with weather forecastdata, user thermostat set points, and in the case of demand responseevents, updated set points to reflect the particular strategy proposedto be deployed. It should be appreciated that techniques other than aGenetic Algorithm can be used within the scope of invention.

HVAC Power Disaggregation

The power required to run standard air conditioners is generallydependent on the outdoor air temperature. Air conditioners utilize avapor compression cycle and achieve cooling by absorbing heat from theindoor environment in the evaporator and rejecting it outside in thecondenser. To get this heat transfer in the condenser, the refrigerantneeds to be hotter than the outdoor air. Modern systems then compensatefor variable outdoor air temperatures by adjusting the difference inpressure between the evaporator and condenser. When the outdoortemperature rises, this pressure differential (i.e., pressure ratio)needs to increase, requiring more power by the compressor. The samepower variability with outdoor temperature is also observed in heatpumps.

This temperature dependence is important for predicting air conditionerload, and can be measured using thermostat and smart meter power data.An exemplary method has been developed that matches the smart meter datawith HVAC ON/OFF time periods to determine approximate HVAC ON powerspikes. These power spikes are binned by their outdoor air temperature.Then a linear regression of the binned data is used to create an HVACpower curve. This power curve can be used to approximate the loadanytime the HVAC is on given outdoor temperature data or forecasts.

FIG. 4 is a diagram showing power usage and temperature readings asdetermined by predictions of the system 100 in comparison to actualpower usage and temperature readings for an example building over anexample time period. In the graph of FIG. 4, line 402 represents theaverage actual power usage, line 404 represents the average power usageprediction as determined by the system 100, line 406 represents theaverage actual indoor temperature and line 408 represents the averageindoor temperature prediction as determined by the system 100. The datadepicted in FIG. 4 was captured during a demand response event. As shownin FIG. 4, the techniques described herein provide accurate predictionsof demand response capacity and the impact of demand response on indoorcomfort characteristics, such as temperature. The deviations betweenactual and predicted values for both power (e.g., 402, 404) and indoortemperature (e.g., 406, 408) are small and demonstrate the effectivenessof the system 100 in providing accurate predictions.

Detailed Measurements from Sensors and Related Analysis

As described previously, the sensors 107 of the system 100 of FIG. 1 canprovide information that enables the system 100 to provide detailedenergy efficiency, comfort analysis, and temperature analysis ofspecific rooms in a building and/or specific doors or windows. FIG. 5Ais a diagram showing temperature readings of a sensor located inproximity to a door as compared to temperature readings of thebuilding's thermostat and of the outdoor environment over the same timeperiod. As shown in FIG. 5A, the door sensor records an increase intemperature 502 (e.g., from 75° at 12:00 am to 88° at 12:00 pm) thatcorresponds to the temperature increase outside the building 504 duringthe same time period. However, the thermostat in the building does notrecord an appreciable temperature change 506 in that time period. Thiscould indicate that the door is experiencing an infiltration problemthat causes an overall energy loss in the building. The system 100 cangenerate a report such as a scorecard including the diagram in FIG. 5Aand transmit the report to a user (e.g., homeowner) along with anindication of what action to take to save energy (i.e., seal the door).For example, FIG. 7 is a scorecard showing energy efficiency and energyusage for a building, where the scorecard is generated by the system ofFIG. 1 using the analysis described herein.

In contrast, FIG. 5B is a diagram showing temperature readings of asensor located in proximity to a door as compared to temperaturereadings of the building's thermostat and of the outdoor environmentover the same time period, where the temperature readings of the doorsensor do not show a temperature increase 514 that corresponds to thetemperature change in the outdoor environment 512. Instead, the doortemperature remains constant throughout the day, much like thethermostat temperature 516. This indicates that the door is notexperiencing an infiltration problem.

FIG. 6 is a diagram showing temperature readings received from sensorslocated in four different rooms of a building as compared to temperaturereadings of the building's thermostat and of the outdoor environmentover the same time period, where the temperature readings of one room604 show an increase in temperature as the outdoor temperature 602increases—while the temperature of the remaining rooms 604, 608, 612correspond to the temperature reading of the thermostat 606. Thisindicates that one room (604) is not receiving the same amount ofcooling as the other rooms in the buildings, which might suggest aproblem with the cooling equipment in that room.

In another example, the system 100 can adjust the energy control pointsfor a building based upon the location and/or distance of an occupant.For example, a homeowner with a mobile device can instruct the servercomputing device 102 to begin adjusting the comfort characteristics ofhis home as the homeowner leaves work for the day. The server computingdevice 102 can determine that the homeowner typically has a one-hourcommute (based upon distance and expected travel time due to traffic,etc.) and the server computing device 102 can generate energy controlpoints for comfort devices 106 in the home to operate so that the homereaches a desired comfort level at approximately the same time that thehomeowner arrives there.

Other Types of Energy-Generating Devices

In addition to being connected to a utility such as a power grid, abuilding may have other types of energy-generating devices installedfrom which it can draw energy to supply to the cooling/heating systemand other comfort devices of the building. Such energy-generatingdevices include solar panels, generators, and energy storage devices.The system 100 described herein can utilize the energy available fromsuch devices or sources in optimizing the energy consumption of thebuilding as described previously. For example, in a building equippedwith solar panels, the system 100 can determine that the building shouldbe cooled a few additional degrees (using energy from the building'ssolar panels) for a period of time during which weather reports havepredicted the sun will be out—because the system 100 has also determinedthat it will be cloudy and warmer later on in the day and that energyprices will rise during the day as well. The advance coolinginstantiated by the system 100 makes use of a cheaper source of energy(solar panels) and takes advantage of the energy optimization andprediction processes described herein.

Application of the Thermodynamic Model to Economic Power Grid LoadShifting

In energy markets, there are potential large savings to be made byshifting the load from periods of high energy prices to those with lowerones. The savings occur because the retail electric provider (REP) canthen procure less expensive power to meet the customer's daily demand.This task of how to best shift the load is challenging in that bothmarket prices as well as customer loads are uncertain. This concept ofshifting customers' loads is known as demand response (DR), wherecustomers' power consumption is adjusted during certain time periods(also called DR events). Certain optimization models (such as stochasticmodels) have been developed and implemented in order to maximizeexpected REP profits and minimize the risk of low profits. A key to theeffective implementation of from these models is the home thermodynamicmodel described above, which serves as the ultimate simulation engine toaccurately forecast where the load is going and to figure out what isrequired to meet the desired load shape for the forecasted marketconditions. Allowing for DR events in which participating customers'thermostats can be automatically and optimally controlled is a majorstep forward in terms of a connected, smart-grid approach to resourcemanagement in power markets. The anticipated optimization system foreconomic-based demand response is a crucial component to moreeconomically efficient matching of energy supply and demand.

FIG. 8 is a block diagram of a system 800 for determining a load controlschedule for a plurality of thermostats in one or more buildings using aload shifting optimization model and applying the load control scheduleto adjust the plurality of thermostats. The system 800 includes a servercomputing device 802 with a load forecasting modeler 804, a deviceoptimization and scheduling module 806, a sending module 808, and a datastorage module 810. The server computing device 802 is a combination ofhardware, including one or more processors and one or more physicalmemory modules, and specialized software engines that execute on theprocessor of the server computing device 802 to perform the functionsdescribed herein. It should be appreciated that, although FIG. 8 showsthe components (e.g., 804, 806, 808, 810) as within a single servercomputing device 802, in some embodiments the components are distributedon different physical devices without departing from the spirit or scopeof the invention. It should also be appreciated that the components 804,806, 808, 810 can be included in server computing device 102 of FIG. 2and/or combined with corresponding modules in server computing device102. Also, in embodiments where the components are distributed ondifferent physical devices, those devices can reside at the samephysical location or may be dispersed to different physical locations.

In some embodiments, the components 804, 806, 808 are specialized setsof computer software instructions programmed onto a dedicated processorin the server computing device 802 and can includespecifically-designated memory locations and/or registers for executingthe specialized computer software instructions. Further explanation ofthe specific processing performed by the components 804, 806, 808 isprovided below.

The load forecasting modeler 804 is a specialized computing module thatreceives data from other computing systems (and, in some embodiments,from data storage 810), and executes a load forecasting model todetermine one or more load/price probability distributions that wouldresult in a preferred set of load-shifting decision rules. In thiscontext, the preferred set of load-shifting decision rules providebetter (and in some cases, optimal) profit for REPs while also reducingrisk associated with unexpected periods of increased energy prices. Theload-shifting decision rules generated by the load forecasting modeler804 are then used to adjust thermostats and/or comfort devices for homesand other buildings serviced by the REPs in order to accomplish theload-shifting and profit/risk optimization determined by the modeler804. The load forecasting modeler 804 receives several types of data,including information relating to the thermodynamic grey-box modelsgenerated by the system 200 of FIG. 2 (as described above), energy pricedata (e.g., historical energy price data, per-hour price probabilitydata, real-time price data, and the like), energy load data (e.g., loadremoved by house/building, historical load data, real-time load data,and the like), weather data (e.g., real-time current conditions,forecast data, and the like) to generate market and REP aggregateforecasts (i.e., sum of individual houses/buildings using theircorresponding thermodynamic models). In some embodiments, the modeler804 also receives other types of data, including the data types receivedby the data receiving module 202 of FIG. 2. Further details regarding anexemplary modeling technique employed by the modeler 804 will bedescribed later in the specification.

The device optimization and scheduling module 806 receives thedetermined load/price probability distributions from the loadforecasting modeler 804 and converts one or more of the load/priceprobability distributions into specific load-shifting decision rules andcorresponding instructions (e.g., a schedule of setpoints over aparticular time period) for each of one or more thermostats and/orcomfort devices that, when executed by such devices, achieve thepreferred price/risk optimization suggested by the load/priceprobability distributions from the modeler 804. It should be appreciatedthat in some embodiments, the functionality of the modeler 804 and thedevice optimization and scheduling module 806 can be combined into asingle module.

The sending module 808 receives the setpoints and related informationfrom the device optimization and scheduling module 806 and transmits thesetpoints to each of the thermostats and/or comfort devices (e.g., thedevices that are in homes and buildings serviced by the REP). As notedabove, the setpoints can comprise a series of control points foroperation of the thermostats and/or comfort devices over a period oftime. For example, the setpoints may comprise a schedule forautomatically adjusting operational settings (e.g., temperature, on/offsetting, and the like) for the thermostats and/or comfort devices for,e.g., a day. In adjusting these operational settings, the thermostatsand/or comfort devices change the demand for energy during particulartimes of day (i.e., peak hours, non-peak hours) so as to optimize profitand minimize risk for the REP.

The data storage module 810 is a database or other similar datastructure, including hardware (e.g., disk drives), software (e.g.,database management programming) or both, that stores informationreceived by the other components 804, 806, 808 of the system 800. Thedata storage 204 also provides data to the components 804, 806, 808, andreceives updated data and analysis from the components 804, 806, 808.

FIG. 9 is a flow diagram of a method 900 for determining a load controlschedule for a plurality of thermostats using a load shiftingoptimization model and applying the load control schedule to adjust theplurality of thermostats, using the system 800 of FIG. 8. The loadforecasting modeler 804 of server computing device 802 receives (902)data representing thermodynamic grey-box models of one or more buildingsserviced by an REP. As mentioned previously, the thermodynamic grey-boxmodels represent complex buildings (that each have unique response tovarious weather conditions and DR setpoint control strategies) as simplegrey-box systems where the sensible energy of the entire indoorenvironment is balanced with the flow of energy through the envelope.Coefficients representing building parameters are used tonon-dimensionalize the heat transfer equations governing this system,each with effective thermal properties. The load forecasting module 804receives an ensemble of thermodynamic models created with thermalproperties for the plurality of thermostats under control. FIG. 10depicts an exemplary building heat transfer model using thethermal-electric analogy.

In a DR event, the actual load removed for each house is a function ofthe weather, thermostat setpoints (and their duration of deviation fromscheduled), home thermal conditions, and occupant comfort tolerances. Toestimate removed and recovery load under a particular DR controlstrategy, the modeler 804 can simulate the energy response usingforecasted and scheduled data.

In addition to the thermodynamic grey-box models, the load forecastingmodule 804 receives (902) energy price data and energy load data, e.g.,from remote computing devices/data feeds provided by third parties, suchas the REPs. The energy price data can comprise historical energy pricedata, such as market settlement data, for particular periods of time(e.g., hours, days, months, etc.). Because the market settlement pricemay not be known with certainty, the modeler 804 generates (904) priceprobability distribution curves based upon the energy price data (e.g.,hour/month combinations). The energy load data can comprise historicalenergy load and demand response data for a particular REP and/orbuildings serviced by the REP. The modeler 804 generates (904) loadprobability distribution curves for load and demand response (e.g., forhour/month combinations).

The load modeler 804 executes (906) a load forecasting model using theinput data elements (i.e., thermodynamic grey-box model(s), energy pricedata, energy load data) using the one or more load probabilitydistribution curves and one or more price probability distributioncurves, along with an energy sale price, for a plurality of demandresponse decision rules to generate one or more profit probabilitydistribution curves. In an exemplary embodiment described below, theload modeler 804 employs a specific stochastic optimization technique togenerate the one or more profit probability distribution curves and todetermine an optimal profit probability distribution curve. It should beappreciated that other types of optimization models (and even differenttypes of stochastic optimization) can be used within the scope ofinvention described herein.

In one embodiment, the load modeler 804 uses a Monte Carlo simulationtechnique that simulates load shifting. The load modeler 804 determinesan optimal load control schedule based on forecasted load, settlementpoint prices, and weather variables but taking into account stochasticload and prices. An optimal schedule is defined as the schedule thatmaximizes the REP's profit and minimizes risk of low profits.

The modeler 804 selects one of a plurality of DR decision rules (orscenarios) to be used in computing the expected profit and conditionalvalue-at-risk (CVaR) (i.e., the measure of the bottom-most profit) basedupon the price and load probability distributions. Exemplary DR decisionrules are set forth to account for subsequent hours of the forecastablefuture. These rules contain all the thermostat setpoint changes that canbe made within the constraints of the device and customer contract. Itshould be appreciated that other types of DR decision rules can beimplemented with the most flexible decision rule being a smooth loadcurve. These rules were selected to minimize compute time for themodeling.

The load modeler 804 then creates a vector of N simulated market pricesfrom an appropriate price distribution using the forecasted load todetermine what distribution is appropriate. Typically, power prices fromone hour to the next should be correlated to some extent given thedrivers of these prices (e.g., weather, supply-demand equilibrium). Tocapture this correlation, the simulated value for the previous hour'sprice, varying for each simulation sample, is the driver of which priceprobability distribution to use. For example, if the previous hour'ssimulated price had a larger probability of being from, e.g., a peakprice distribution, then this was the one that was then simulated forthe next hour. Conversely, if the previous hour's simulated price had alarger probability of being from e.g., a typical day distribution, itwas used. In this way, there is correlation between successive pricesthat are sampled in the simulation. Also, the modeler 804 can calibratethe hourly price distributions from actual price data which werecorrelated, and as a result, the successive hourly parameters reflectthis.

The load modeler 804 creates a vector of N simulated loads from anappropriate load distribution using the forecasted load.

The load modeler 804 then applies the particular demand responsedecision rule selected from the available options, and computes a profitfor each of the simulated price and load values (given a fixed price tosell the electricity to customers). As noted above, the modeler 804analyzes all hours of each modeled day. The modeler 804 calculates theprofit as:Σ_(t=1) ⁸D_(t)(C−P_(t))

where Dt is electricity demand for time (hour) t that is simulated fromone of a plurality of probability distributions for that time, C is theelectricity rate charged to customers independent of time (however, itshould be appreciated that customers can be charged a variable ratewhich is a function of time), and Pt is the settlement point price attime t drawn from one of a plurality of probability distributions foreach time t.

The modeler 804 then generates a probability distribution curve forprofit, using the computed profit values, and in some embodiments, candetermine a base case profit distribution curve (i.e., no loadshifting), a best case profit distribution curve, and a worst caseprofit distribution curve. Each of the best case profit distributioncurve and worst case profit distribution curve are associated with atleast one of the DR decision rules (as shown in the table above). FIG.11 is an exemplary graph of a base case profit distribution, FIG. 12 isan exemplary graph of a best case profit distribution, and FIG. 13 is anexemplary graph of a worst case profit distribution.

The difference between the best load-shifting decision rule (as in FIG.12) and the worst load-shifting decision rule (as in FIG. 13) is mostlyin how the left-side of the profit distribution changes. In the bestcase, the bottom 20% of the profits occurs at about $4311 so there is an80% chance of profits higher than this value. By contrast, the worstload-shifting rule is significantly further to the left at about $4036;the base case is in the middle. The net effect then of picking the best(or optimal) DR decision rule is to pick the DR decision rule thatresults in a profit probability distribution curve that minimizes thedownside risk (DVaR) of load-shifting and moving the left-hand sidetail's probability to the right. The load modeler 804 determines (908)at least one of the profit probability distribution curves that has anoptimal profit value and identifies the DR decision rule that resultedin the profit probability distribution curve.

Once the load modeler 804 has determined the optimal profit probabilitydistribution curve and profit value, and the corresponding DR decisionrule, the modeler 804 transmits the information to the deviceoptimization and scheduling module 806. The device optimization andscheduling module 806 generates (910) a load control schedule for aplurality of energy control devices (e.g., thermostats, comfort devices,and the like) based upon the DR decision rule associated with the profitprobability distribution curve that has an optimal profit value. Forexample, if the DR decision rule is to shift load from hours ending 14,15, and 16 to hour 17, then the device optimization and schedulingmodule 806 generates a load control schedule that results in shiftingthe load associated with hours 14, 15, and 16 to hour 17 for the groupsof houses most efficient at delivering the load. In one example, anactual DR event gets triggered either manually or automatically basedupon, e.g., threshold values set by the REP for profit, CVaR, and DVaRat a minimum. Other triggers can be envisioned within the scope ofinvention, such as forecasted load which exceeds the amount of energythe REP purchased in the day-ahead markets (i.e., so that the REP doesnot have to buy energy on the spot market, which may be more expensive).The REP can also trigger a DR event based on other pre-defined rulesand/or thresholds can be set to notify REPs (and in some embodiments,individual customers for each REP) such that the REPs/customers canmanually respond or accept a recommended DR event.

In some embodiments, the REP may provide other data elements, such asmaximum number of DR days (i.e., to prevent customer opt-out) and/ormaximum number of DR hours. In one example, suppose that the REP set amaximum of five DR events for the summer and four DR events have alreadybeen used. It is getting into late August and the long range weatherforecast is for cooler days. The REP may still choose to do a DR eventand take less profit at the end of the summer knowing that much coolerdays are ahead.

The optimization and scheduling module 806 generates (912) operationalparameters for the plurality of energy control devices using the loadcontrol schedule. For example, the optimization and scheduling module806 creates operational parameters like current and future temperaturesetpoint schedules, current and future operational settings (e.g.,on/off, power level, etc.) for comfort devices, and so forth.

The sending module 808 receives the operational parameters from thedevice optimization and scheduling module 806, and transmits (914) theoperational parameters to individual thermostats and/or comfort devicescoupled to the system 800. Upon receipt, the thermostats and/or comfortdevices automatically adjust their operational settings so that theoverall energy demand for the buildings serviced by the REP conforms tothe optimal profit distribution curve, thereby maximizing the profit forthe REP (i.e., enabling the REP to purchase energy it will need duringlower cost periods of time) and minimizing the risk for the REP that itwill be required to purchase energy during higher cost periods of time.

It should be appreciated that there are at least three benefits ofreducing peak load via DR programs. First, there are economic benefitsfrom preferentially purchasing electricity during off-peak times whenprices are lower. Second, there are reliability benefits because withlower peak loads, there is less need for a high percentage of systemcapacity to be online. Third, there are environmental benefits becausedemand response can decrease the overall amount of electricity used, sothere are benefits in terms of conserving resources and reducinggreenhouse gas emissions. In addition to these benefits, DR can saveelectricity consumers billions of dollars per year.

It should be further appreciated that, in addition to thermostats andother energy control devices as described above, the techniquesdescribed herein are equally applicable to adjust the operatingparameters of many other energy-related and/or energy-consuming devicesin a home or building for economic demand response. For example, otherlarge home appliances such as washers, dryers, hot water heaters, andpool pumps that are capable of being controlled remotely or otherwise incommunication with remote devices that can provide operatinginstructions to the devices can be coupled to the system in order totake advantage of the demand response and load shifting principles.

The above-described techniques can be implemented in digital and/oranalog electronic circuitry, or in computer hardware, firmware,software, or in combinations of them. The implementation can be as acomputer program product, i.e., a computer program tangibly embodied ina machine-readable storage device, for execution by, or to control theoperation of, a data processing apparatus, e.g., a programmableprocessor, a computer, and/or multiple computers. A computer program canbe written in any form of computer or programming language, includingsource code, compiled code, interpreted code and/or machine code, andthe computer program can be deployed in any form, including as astand-alone program or as a subroutine, element, or other unit suitablefor use in a computing environment. A computer program can be deployedto be executed on one computer or on multiple computers at one or moresites.

Method steps can be performed by one or more special-purpose processorsexecuting a computer program to perform functions of the invention byoperating on input data and/or generating output data. Method steps canalso be performed by, and an apparatus can be implemented as, specialpurpose logic circuitry, e.g., a FPGA (field programmable gate array), aFPAA (field-programmable analog array), a CPLD (complex programmablelogic device), a PSoC (Programmable System-on-Chip), ASIP(application-specific instruction-set processor), or an ASIC(application-specific integrated circuit), or the like. Subroutines canrefer to portions of the stored computer program and/or the processor,and/or the special circuitry that implement one or more functions.

Processors suitable for the execution of a computer program include, byway of example, special purpose microprocessors. Generally, a processorreceives instructions and data from a read-only memory or a randomaccess memory or both. The essential elements of a computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and/or data. Memory devices, such as a cache, canbe used to temporarily store data. Memory devices can also be used forlong-term data storage. Generally, a computer also includes, or isoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. A computer can also beoperatively coupled to a communications network in order to receiveinstructions and/or data from the network and/or to transferinstructions and/or data to the network. Computer-readable storagemediums suitable for embodying computer program instructions and datainclude all forms of volatile and non-volatile memory, including by wayof example semiconductor memory devices, e.g., DRAM, SRAM, EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and optical disks,e.g., CD, DVD, HD-DVD, and Blu-ray disks. The processor and the memorycan be supplemented by and/or incorporated in special purpose logiccircuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computer in communication with a display device,e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystal display)monitor, for displaying information to the user and a keyboard and apointing device, e.g., a mouse, a trackball, a touchpad, or a motionsensor, by which the user can provide input to the computer (e.g.,interact with a user interface element). Other kinds of devices can beused to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, and/ortactile input.

The above described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributed computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The above describedtechniques can be implemented in a distributed computing system thatincludes any combination of such back-end, middleware, or front-endcomponents.

The components of the computing system can be interconnected bytransmission medium, which can include any form or medium of digital oranalog data communication (e.g., a communication network). Transmissionmedium can include one or more packet-based networks and/or one or morecircuit-based networks in any configuration. Packet-based networks caninclude, for example, the Internet, a carrier internet protocol (IP)network (e.g., local area network (LAN), wide area network (WAN), campusarea network (CAN), metropolitan area network (MAN), home area network(HAN)), a private IP network, an IP private branch exchange (IPBX), awireless network (e.g., radio access network (RAN), Bluetooth, Wi-Fi,WiMAX, general packet radio service (GPRS) network, HiperLAN), and/orother packet-based networks. Circuit-based networks can include, forexample, the public switched telephone network (PSTN), a legacy privatebranch exchange (PBX), a wireless network (e.g., RAN, code-divisionmultiple access (CDMA) network, time division multiple access (TDMA)network, global system for mobile communications (GSM) network), and/orother circuit-based networks.

Information transfer over transmission medium can be based on one ormore communication protocols. Communication protocols can include, forexample, Ethernet protocol, Internet Protocol (IP), Voice over IP(VOIP), a Peer-to-Peer (P2P) protocol, Hypertext Transfer Protocol(HTTP), Session Initiation Protocol (SIP), H.323, Media Gateway ControlProtocol (MGCP), Signaling System #7 (SS7), a Global System for MobileCommunications (GSM) protocol, a Push-to-Talk (PTT) protocol, a PTT overCellular (POC) protocol, and/or other communication protocols.

Devices of the computing system can include, for example, a computer, acomputer with a browser device, a telephone, an IP phone, a mobiledevice (e.g., cellular phone, personal digital assistant (PDA) device,laptop computer, electronic mail device), and/or other communicationdevices. The browser device includes, for example, a computer (e.g.,desktop computer, laptop computer) with a World Wide Web browser (e.g.,Microsoft® Internet Explorer® available from Microsoft Corporation,Mozilla® Firefox available from Mozilla Corporation). Mobile computingdevice include, for example, a Blackberry®. IP phones include, forexample, a Cisco® Unified IP Phone 7985G available from Cisco Systems,Inc, and/or a Cisco® Unified Wireless Phone 7920 available from CiscoSystems, Inc.

Comprise, include, and/or plural forms of each are open ended andinclude the listed parts and can include additional parts that are notlisted. And/or is open ended and includes one or more of the listedparts and combinations of the listed parts.

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theinvention described herein.

What is claimed is:
 1. A method for determining a load control schedulefor a plurality of energy control devices using a load shiftingoptimization model and applying the load control schedule to adjust theplurality of energy control devices, the method comprising: receiving,by a server computing device, one or more thermodynamic grey-box models,wherein each thermodynamic grey-box model is associated with one or morebuildings; receiving, by the server computing device, energy price dataassociated with a utility provider servicing the one or more buildingsand energy load forecast data associated with the one or more buildings;generating, by the server computing device, one or more priceprobability distribution curves based upon the energy price data;generating, by the server computing device, one or more load probabilitydistribution curves based upon the energy load forecast data and thethermodynamic grey-box models; executing, by the server computingdevice, a load shifting optimization model using the one or more priceprobability distribution curves, the one or more load probabilitydistribution curves, and an energy sale price to determine a profitprobability distribution curve for each of a plurality of demandresponse decision rules; determining, by the server computing device, atleast one of the profit probability distribution curves that has anoptimal profit value; generating, by the server computing device, a loadcontrol schedule for each of a plurality of energy control devicescoupled to the server computing device based upon the profit probabilitydistribution curve that has the optimal profit value; generating, by theserver computing device, one or more operational parameters for each ofthe plurality of energy control devices using the load control schedule;and transmitting, by the server computing device, the one or moreoperational parameters to the plurality of energy control devices,wherein the energy control devices adjust one or more of current andfuture operational parameters based upon the received operationalparameters.
 2. The method of claim 1, wherein each thermodynamicgrey-box model is based upon one or more characteristics of an indoorenvironment of a building and a flow of energy through an envelope ofthe building.
 3. The method of claim 1, wherein the energy price data isone or more of historical energy price data and per-hour priceprobability data.
 4. The method of claim 1, wherein the energy loadforecast data is one or more of historical load data and load forecastprobability data.
 5. The method of claim 1, wherein the step ofexecuting the load shifting optimization model comprises: determining,by the server computing device, a demand response decision rule;generating, by the server computing device, a vector of simulated marketprices using at least one of the price probability distribution curves;generating, by the server computing device, a vector of simulated loadsusing at least one of the load probability distribution curves;applying, by the server computing device, the demand response decisionrule to the vector of simulated market prices and the vector ofsimulated loads to generate the profit probability distribution curvefor the demand response decision rule; and determining, by the servercomputing device, a profit value associated with the profit probabilitydistribution curve using the energy sale price.
 6. The method of claim5, further comprising repeating, by the server computing device, theexecution of the load shifting optimization model for each combinationof the plurality of demand response decision rules, the one or moreprice probability distribution curves, and the one or more loadprobability distribution curves to generate a plurality of profitvalues.
 7. The method of claim 6, wherein the step of determining atleast one of the profit probability distribution curves that has anoptimal profit value comprises selecting the profit probability curvethat is associated with a maximum profit value.
 8. The method of claim5, further comprising determining, by the server computing device, arisk value associated with the profit probability distribution curveusing the energy sale price.
 9. The method of claim 8, wherein the stepof determining at least one of the profit probability distributioncurves that has an optimal profit value comprises selecting the profitprobability curve that is associated with a minimum risk value.
 10. Themethod of claim 1, wherein the one or more operational parameters foreach of a plurality of energy control devices comprise thermostatsetpoints.
 11. The method of claim 10, wherein the thermostat setpointscomprise a schedule of current and future temperature settings for thethermostat.
 12. The method of claim 1, wherein the one or moreoperational parameters for each of a plurality of energy control devicescomprise operational settings for a comfort device.
 13. The method ofclaim 12, wherein the operational settings for a comfort device comprisea schedule of current and future operational settings for the comfortdevice.
 14. A system for determining a load control schedule for aplurality of energy control devices using a load shifting optimizationmodel and applying the load control schedule to adjust the plurality ofenergy control devices, the system comprising: a server computing devicethat receives one or more thermodynamic grey-box models, wherein eachthermodynamic grey-box model is associated with one or more buildings;receives energy price data associated with a utility provider servicingthe one or more buildings and energy load forecast data associated withthe one or more buildings; generates one or more price probabilitydistribution curves based upon the energy price data; generates one ormore load probability distribution curves based upon the energy loadforecast data; executes a load shifting optimization model using the oneor more price probability distribution curves, the one or more loadprobability distribution curves, and an energy sale price to determine aprofit probability distribution curve for each of a plurality of demandresponse decision rules; determines at least one of the profitprobability distribution curves that has an optimal profit value;generates a load control schedule for each of a plurality of energycontrol devices coupled to the server computing device based upon theprofit probability distribution curve that has the optimal profit value;generates one or more operational parameters for each of the pluralityof energy control devices using the load control schedule; and transmitsthe one or more operational parameters to the plurality of energycontrol devices, wherein the energy control devices adjust one or moreof current and future operational parameters based upon the receivedoperational parameters.
 15. The system of claim 14, wherein eachthermodynamic grey-box model is based upon one or more characteristicsof an indoor environment of a building and a flow of energy through anenvelope of the building.
 16. The system of claim 14, wherein the energyprice data is one or more of historical energy price data and per-hourprice probability data.
 17. The system of claim 14, wherein the energyload forecast data is one or more of historical load data and loadforecast probability data.
 18. The system of claim 14, wherein the stepof executing the load shifting optimization model comprises:determining, by the server computing device, a demand response decisionrule; generating, by the server computing device, a vector of simulatedmarket prices using at least one of the price probability distributioncurves; generating, by the server computing device, a vector ofsimulated loads using at least one of the load probability distributioncurves; applying, by the server computing device, the demand responsedecision rule to the vector of simulated market prices and the vector ofsimulated loads to generate the profit probability distribution curvefor the demand response decision rule; and determining, by the servercomputing device, a profit value associated with the profit probabilitydistribution curve using the energy sale price.
 19. The system of claim18, wherein the server computing device repeats the execution of theload shifting optimization model for each combination of the pluralityof demand response decision rules, the one or more price probabilitydistribution curves, and the one or more load probability distributioncurves to generate a plurality of profit values.
 20. The system of claim19, wherein determining at least one of the profit probabilitydistribution curves that has an optimal profit value comprises selectingthe profit probability curve that is associated with a maximum profitvalue.
 21. The system of claim 18, wherein the server computing devicedetermines a risk value associated with the profit probabilitydistribution curve using the energy sale price.
 22. The system of claim21, wherein determining at least one of the profit probabilitydistribution curves that has an optimal profit value comprises selectingthe profit probability curve that is associated with a minimum riskvalue.
 23. The system of claim 14, wherein the one or more operationalparameters for each of a plurality of energy control devices comprisethermostat setpoints.
 24. The system of claim 23, wherein the thermostatsetpoints comprise a schedule of current and future temperature settingsfor the thermostat.
 25. The system of claim 14, wherein the one or moreoperational parameters for each of a plurality of energy control devicescomprise operational settings for a comfort device.
 26. The system ofclaim 25, wherein the operational settings for a comfort device comprisea schedule of current and future operational settings for the comfortdevice.